Assessing Measurement Invariance for Longitudinal Data through Latent Markov Models. Issue 3 (4th May 2022) - Di Mari, Roberto; Dotto, Francesco; Farcomeni, Alessio; Punzo, Antonio
ABSTRACT: We propose a general approach to detect measurement non-invariance in latent Markov models for longitudinal data. We define different notions of differential item functioning in the context of panel data. We then present a model selection approach based on the Bayesian information criterion (BIC) to choose both the number of latent states and the measurement structure. We show the practical relevance by means of an extensive simulation study, and illustrate its use on two real–data examples from the social sciences. Our results indicate that BIC is able to select the correct measurement equivalence structure more than 95% of times.